Elevator group-control policy based on neural network optimized by genetic algorithm |
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Authors: | Hong Shen Jianru Wan Zhichao Zhang Yingpei Liu Guangye Li |
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Institution: | 1. School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China 2. Baoding Power Supply Company, Baoding 071000, China |
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Abstract: | Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid
algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the
weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are
three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights
are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control
of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control
system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation
performance. |
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Keywords: | elevator group control genetic algorithm neural network hybrid algorithm |
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